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|STAT 54600 - Computational Statistics|
Learning Outcomes: 1. Master commonly used numerical matrix operations, including the sweep operator, Cholesky decomposition, eigenvalue decomposition, and single value decomposition. 2. Understand statistical thinking in development of interactive numerical methods. 3. Apply optimization algorithms such as quasi-Newton and conjugate gradient methods. 4. Implement EM-type algorithms for maximum likelihood estimation when expanded complete-data models are available, including commonly used statistical models. 5. Create random number generators. 6. Implement the Gibbs sampler and Metropolis-Hastings algorithms for Bayesian data analysis.
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